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ORCIDKamil Adamczewski
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2020 – today
- 2024
[j2]Yi Yang, Kamil Adamczewski, Xiaoxiao Li, Danica J. Sutherland, Mijung Park:
Differentially Private Neural Tangent Kernels (DP-NTK) for Privacy-Preserving Data Generation. J. Artif. Intell. Res. 81: 683-700 (2024)- [c12]Tomasz Trzcinski
, Bartlomiej Twardowski, Bartosz Zielinski, Kamil Adamczewski, Bartosz Wójcik:
Zero-Waste Machine Learning. ECAI 2024: 43-49 - [c11]Adam Pardyl, Michal Wronka, Maciej Wolczyk, Kamil Adamczewski, Tomasz Trzcinski, Bartosz Zielinski:
AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale. ECCV (21) 2024: 112-129 - [c10]Jan Ludziejewski, Jakub Krajewski, Kamil Adamczewski, Maciej Pióro, Michal Krutul, Szymon Antoniak, Kamil Ciebiera, Krystian Król, Tomasz Odrzygózdz, Piotr Sankowski, Marek Cygan, Sebastian Jaszczur:
Scaling Laws for Fine-Grained Mixture of Experts. ICML 2024 - [i15]Jakub Krajewski, Jan Ludziejewski, Kamil Adamczewski, Maciej Pióro, Michal Krutul, Szymon Antoniak, Kamil Ciebiera, Krystian Król, Tomasz Odrzygózdz, Piotr Sankowski, Marek Cygan, Sebastian Jaszczur:
Scaling Laws for Fine-Grained Mixture of Experts. CoRR abs/2402.07871 (2024) - [i14]Adam Pardyl, Michal Wronka, Maciej Wolczyk, Kamil Adamczewski, Tomasz Trzcinski, Bartosz Zielinski:
AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale. CoRR abs/2404.03482 (2024) - [i13]Bartlomiej Krzepkowski, Monika Michaluk, Franciszek Szarwacki, Piotr Kubaty, Jary Pomponi, Tomasz Trzcinski, Bartosz Wójcik, Kamil Adamczewski:
Joint or Disjoint: Mixing Training Regimes for Early-Exit Models. CoRR abs/2407.14320 (2024) - [i12]Kamil Adamczewski, Yawei Li, Luc Van Gool:
Shapley Pruning for Neural Network Compression. CoRR abs/2407.15875 (2024) - 2023
- [b1]Kamil Adamczewski:
Less is More: Discovering Redundancies in Data and Neural Network Models for Improved Efficiency, Intrerpretability and Privacy. ETH Zurich, Zürich, Switzerland, 2023 - [i11]Yilin Yang, Kamil Adamczewski, Danica J. Sutherland, Xiaoxiao Li, Mijung Park:
Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation. CoRR abs/2303.01687 (2023) - [i10]Kamil Adamczewski, Mijung Park:
Differential Privacy Meets Neural Network Pruning. CoRR abs/2303.04612 (2023) - [i9]Kamil Adamczewski, Christos Sakaridis, Vaishakh Patil, Luc Van Gool:
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion. CoRR abs/2303.11720 (2023) - [i8]Kamil Adamczewski, Yingchen He, Mijung Park:
Pre-Pruning and Gradient-Dropping Improve Differentially Private Image Classification. CoRR abs/2306.11754 (2023) - 2022
- [c9]Kamil Adamczewski, Christos Sakaridis, Vaishakh Patil, Luc Van Gool:
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion. CoRL 2022: 561-570 - [c8]Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, Luc Van Gool:
Revisiting Random Channel Pruning for Neural Network Compression. CVPR 2022: 191-201 - [c7]Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park:
Hermite Polynomial Features for Private Data Generation. ICML 2022: 22300-22324 - [i7]Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, Luc Van Gool:
Revisiting Random Channel Pruning for Neural Network Compression. CoRR abs/2205.05676 (2022) - 2021
- [c6]Frederik Harder, Kamil Adamczewski, Mijung Park:
DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation. AISTATS 2021: 1819-1827 - [c5]Kamil Adamczewski, Mijung Park:
Dirichlet Pruning for Convolutional Neural Networks. AISTATS 2021: 3637-3645 - 2020
- [c4]ChangYong Oh, Kamil Adamczewski, Mijung Park:
Radial and Directional Posteriors for Bayesian Deep Learning. AAAI 2020: 5298-5305 - [i6]Frederik Harder, Kamil Adamczewski, Mijung Park:
Differentially Private Mean Embeddings with Random Features (DP-MERF) for Simple & Practical Synthetic Data Generation. CoRR abs/2002.11603 (2020) - [i5]Kamil Adamczewski, Frederik Harder, Mijung Park:
Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning. CoRR abs/2010.13872 (2020) - [i4]Kamil Adamczewski, Mijung Park:
Dirichlet Pruning for Neural Network Compression. CoRR abs/2011.05985 (2020)
2010 – 2019
- 2019
- [i3]ChangYong Oh, Kamil Adamczewski, Mijung Park:
Radial and Directional Posteriors for Bayesian Neural Networks. CoRR abs/1902.02603 (2019) - [i2]Kamil Adamczewski, Mijung Park:
Neuron ranking - an informed way to condense convolutional neural networks architecture. CoRR abs/1907.02519 (2019) - 2015
- [j1]Kamil Adamczewski, Enrique Treviño:
The Smoothed Pólya-Vinogradov Inequality. Integers 15: A20 (2015) - [c3]Yumin Suh, Kamil Adamczewski, Kyoung Mu Lee:
Subgraph matching using compactness prior for robust feature correspondence. CVPR 2015: 5070-5078 - [c2]Kamil Adamczewski, Yumin Suh, Kyoung Mu Lee:
Discrete Tabu Search for Graph Matching. ICCV 2015: 109-117 - 2014
- [c1]Kamil Adamczewski, Szymon Matejczyk, Tomasz P. Michalak:
How good is the Shapley value-based approach to the influence maximization problem? ECAI 2014: 955-956 - [i1]Kamil Adamczewski, Szymon Matejczyk, Tomasz P. Michalak:
How good is the Shapley value-based approach to the influence maximization problem? CoRR abs/1409.7830 (2014)
Coauthor Index
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last updated on 2025-01-16 23:13 CET by the dblp team
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